{
 "cells": [
  {
   "cell_type": "raw",
   "id": "5c9c3882",
   "metadata": {},
   "source": [
    "数据：csv类型的数据。csv就是一个以逗号为列分隔符的文本文件。"
   ]
  },
  {
   "cell_type": "raw",
   "id": "b1aaafa8",
   "metadata": {},
   "source": [
    "numpy、pandas:读取数据、查看数据基本信息(数据理解)和简单数据处理的(合并、删除、填充)。pandas居多\n",
    "matpplotlib、seaborn:绘图\n",
    "sklearn:数据预处理、建模(分类、回归、聚类)、模型评估\n",
    "mlxtend:建模(关联规则)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ef1aa08f",
   "metadata": {},
   "source": [
    "read_csv()读取上来的数据是一个DataFrame类型。类似于一个表格，提供大量的针对行和列的方法"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0457b74",
   "metadata": {},
   "source": [
    "1.读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f02663b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f01d3bb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('C:/Users/lenovo/BigData/PythonBase/titanic_trains.csv') #路径：绝对路径\n",
    "df = pd.read_csv('./titanic_trains.csv') #相对路径都支持,可以按快捷键tab补全路径\n",
    "df.head() #查看数据的前5行"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70ea4806",
   "metadata": {},
   "source": [
    "2.查看数据的基本信息，即查看csv文件的数据信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4dde1dde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape  #查看数据的行列数，返回值是一个元组"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21bbebb0",
   "metadata": {},
   "source": [
    "3.查看缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "50a7bd55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看缺失值数量 \n",
    "# df.isnull() #查看缺失值\n",
    "df.isnull().sum() #统计每一列中缺失值的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "34dcb45c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    False\n",
       "Survived       False\n",
       "Pclass         False\n",
       "Name           False\n",
       "Sex            False\n",
       "Age             True\n",
       "SibSp          False\n",
       "Parch          False\n",
       "Ticket         False\n",
       "Fare           False\n",
       "Cabin           True\n",
       "Embarked        True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看是否存在缺失值\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "831dc52b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId     0.000000\n",
       "Survived        0.000000\n",
       "Pclass          0.000000\n",
       "Name            0.000000\n",
       "Sex             0.000000\n",
       "Age            19.865320\n",
       "SibSp           0.000000\n",
       "Parch           0.000000\n",
       "Ticket          0.000000\n",
       "Fare            0.000000\n",
       "Cabin          77.104377\n",
       "Embarked        0.224467\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看缺失值占比\n",
    "df.isnull().sum()/df.shape[0]*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9e7b311",
   "metadata": {},
   "source": [
    "4.missingno是一个基于Python的开源数据可视化工具，旨在帮助数据分析师和科学家更好地理解和处理数据缺失。\n",
    "该模块提供了一系列函数和方法，可以用于可视化缺失数据的分布、关联性和模式。\n",
    "missingno模块可以与Pandas和Seaborn等常用数据处理和可视化工具无缝集成，使得数据缺失的分析和处理更加高效和便捷。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2f7b71dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: missingno in c:\\users\\lenovo\\appdata\\roaming\\python\\python310\\site-packages (0.5.2)\n",
      "Requirement already satisfied: seaborn in d:\\soft\\anaconda\\lib\\site-packages (from missingno) (0.12.2)\n",
      "Requirement already satisfied: numpy in d:\\soft\\anaconda\\lib\\site-packages (from missingno) (1.23.5)\n",
      "Requirement already satisfied: matplotlib in d:\\soft\\anaconda\\lib\\site-packages (from missingno) (3.7.0)\n",
      "Requirement already satisfied: scipy in d:\\soft\\anaconda\\lib\\site-packages (from missingno) (1.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in d:\\soft\\anaconda\\lib\\site-packages (from matplotlib->missingno) (1.4.4)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in d:\\soft\\anaconda\\lib\\site-packages (from matplotlib->missingno) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in d:\\soft\\anaconda\\lib\\site-packages (from matplotlib->missingno) (2.8.2)\n",
      "Requirement already satisfied: packaging>=20.0 in d:\\soft\\anaconda\\lib\\site-packages (from matplotlib->missingno) (22.0)\n",
      "Requirement already satisfied: cycler>=0.10 in d:\\soft\\anaconda\\lib\\site-packages (from matplotlib->missingno) (0.11.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in d:\\soft\\anaconda\\lib\\site-packages (from matplotlib->missingno) (9.4.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in d:\\soft\\anaconda\\lib\\site-packages (from matplotlib->missingno) (1.0.5)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in d:\\soft\\anaconda\\lib\\site-packages (from matplotlib->missingno) (4.25.0)\n",
      "Requirement already satisfied: pandas>=0.25 in d:\\soft\\anaconda\\lib\\site-packages (from seaborn->missingno) (1.5.3)\n",
      "Requirement already satisfied: pytz>=2020.1 in d:\\soft\\anaconda\\lib\\site-packages (from pandas>=0.25->seaborn->missingno) (2022.7)\n",
      "Requirement already satisfied: six>=1.5 in d:\\soft\\anaconda\\lib\\site-packages (from python-dateutil>=2.7->matplotlib->missingno) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install missingno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0e8a977d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import missingno as msg\n",
    "\n",
    "msg.bar(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "99745bc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msg.matrix(df) #缺失值的地方显示不一样"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f24ebbdf",
   "metadata": {},
   "source": [
    "4.1缺失值的处理-删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "19f9faca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按列删除(遵循80%法则)如果一列的非缺失部分低于80%则可以考虑删除该列，也就是缺失部分超过20%\n",
    "#axis按列还是行删除 how全部删除还是部分删除 thresh(非缺失值部分数据是多少，891是数据行，0.8是非缺失值占比)，inplace参数表示是否对原始原始\n",
    "#变量进行修改，Ture修改有返回值，false表示不修改并且没有返回值\n",
    "# df_drop = df.dropna(axis='columns',how='any',thresh=891*0.8) #3.10版本不支持how和thresh参数同时出现\n",
    "df_drop = df.dropna(axis='columns',thresh=891*0.8,inplace=False) #axis也可以是1，0，1代表列，0代表行\n",
    "#删掉缺失值占比超过20%的列之后，再看占比情况\n",
    "df_drop.isnull().sum()/df.shape[0]*100\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a3b650c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0.0\n",
       "Survived       0.0\n",
       "Pclass         0.0\n",
       "Name           0.0\n",
       "Sex            0.0\n",
       "Age            0.0\n",
       "SibSp          0.0\n",
       "Parch          0.0\n",
       "Ticket         0.0\n",
       "Fare           0.0\n",
       "Embarked       0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按行删除\n",
    "df_drop_row = df_drop.dropna(axis=0,how='any')#按行删除时不能指定thresh参数\n",
    "df_drop_row.isnull().sum()/891*100  #这样删完以后，整个df_drop_row一个缺失值都没有了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc56f81f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(712, 11)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drop_row.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "660e107d",
   "metadata": {},
   "source": [
    "4.2缺失值的处理-填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "542f2ce4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df_re = df.copy() #浅拷贝复制数据,怕后面的操作出现误操作，把df数据给删了\n",
    "df_re.drop(axis=1,columns=['Cabin'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "199fc75e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用replace进行缺失值填充\n",
    "df_re.isnull().sum()\n",
    "df_re.head()\n",
    "#对Embarked列的缺失值进行填充操作,替换成这一列的众数,mode方法获取这一列的众数,np.nan表示缺失值\n",
    "df_re['Embarked'] = df_re['Embarked'].replace(np.nan,df_re['Embarked'].mode()[0]) #列存在则覆盖，不存在则新增一列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "add8fc68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Embarked         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_re.isnull().sum() #Embarked列的缺失值为0了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c524172a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_si = df.copy() #复制数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a8bba5c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: scikit-learn in d:\\soft\\anaconda\\lib\\site-packages (1.2.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in d:\\soft\\anaconda\\lib\\site-packages (from scikit-learn) (1.1.1)\n",
      "Requirement already satisfied: numpy>=1.17.3 in d:\\soft\\anaconda\\lib\\site-packages (from scikit-learn) (1.23.5)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in d:\\soft\\anaconda\\lib\\site-packages (from scikit-learn) (2.2.0)\n",
      "Requirement already satisfied: scipy>=1.3.2 in d:\\soft\\anaconda\\lib\\site-packages (from scikit-learn) (1.10.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "035117d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用SimpleImputer进行缺失值填充\n",
    "from sklearn.impute import SimpleImputer\n",
    "df_si = df.copy() #复制数据,这里如果不写，用上面的copy，但是要从上面重新运行一次\n",
    "#定义规则，告诉怎么去填充\n",
    "#missing_values:需要填充值，比如所有的1换成2 , strategy:填充的内容，使用什么值进行填充,有均值、众数、中位数三种\n",
    "model_si1 = SimpleImputer(missing_values=np.nan,strategy='mean') \n",
    "SimpleImputer(missing_values=np.nan,stra)\n",
    "#将规则应用到数据\n",
    "# df_si[['Age','Name']]#把一维变成二维\n",
    "# type(df_si['Age']) #pandas.core.frame.Series\n",
    "# type(df_si[['Age']]) #pandas.core.frame.DataFrame\n",
    "# df_si = model_si1.fit_transform(X=df_si['Age'].to_frame())  #转换成二维的df，也可以用下面的方法转为df\n",
    "df_s1 = model_si1.fit_transform(X=df_si[['Age']]) #X要求的数据为二维的数据所以使用两个方括号\n",
    "# type(df_si)\n",
    "# 转型\n",
    "df_s1 = pd.DataFrame(df_s1,columns=['Age']) #将Array转换成DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "22ec2dba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>29.699118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Age\n",
       "0    22.000000\n",
       "1    38.000000\n",
       "2    26.000000\n",
       "3    35.000000\n",
       "4    35.000000\n",
       "..         ...\n",
       "886  27.000000\n",
       "887  19.000000\n",
       "888  29.699118\n",
       "889  26.000000\n",
       "890  32.000000\n",
       "\n",
       "[891 rows x 1 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "92c350bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#定义规则，告诉怎么去填充\n",
    "#missing_values:需要填充值，比如所有的1换成2 , strategy:填充的内容，使用什么值进行填充,有均值、众数、中位数三种\n",
    "model_si2 = SimpleImputer(missing_values=np.nan,strategy='most_frequent')\n",
    "#将规则应用到数据\n",
    "# df_si[['Age','Name']]#把一维变成二维\n",
    "# type(df_si['Age']) #pandas.core.frame.Series\n",
    "# type(df_si[['Age']]) #pandas.core.frame.DataFrame\n",
    "# df_si = model_si1.fit_transform(X=df_si['Age'].to_frame())  #转换成二维的df，也可以用下面的方法转为df\n",
    "df_s2 = model_si2.fit_transform(X=df_si) #X要求的数据为二维的数据所以使用两个方括号\n",
    "# type(df_si)\n",
    "# 转型\n",
    "df_s2 = pd.DataFrame(df_s2,columns=df_si.columns) #将Array转换成DataFrame,并且指定列名\n",
    "df_s2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "23ff772b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_si.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9e5c7b3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 合并数据(覆盖数据),将用均值和众值处理的pd合并，得到最终的处理pd\n",
    "df_s2['Age'] = df_s1['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c5d6b43c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    False\n",
       "Survived       False\n",
       "Pclass         False\n",
       "Name           False\n",
       "Sex            False\n",
       "Age            False\n",
       "SibSp          False\n",
       "Parch          False\n",
       "Ticket         False\n",
       "Fare           False\n",
       "Cabin          False\n",
       "Embarked       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_s2.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ca371c51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNNImputer()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNNImputer</label><div class=\"sk-toggleable__content\"><pre>KNNImputer()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNNImputer()"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import KNNImputer  #通过算法预测进行填充 KNNImputer()方法进行预测填充,后续再说，和SimpleImputer的用法类似\n",
    "KNNImputer()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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